Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The research presented in this thesis serves to provide a tool to autonomously
screen for cardiovascular disease in the rural areas of Africa. Vital information
thus obtained from patients can be communicated to advanced medical centres by
Telemedicine. Cardiovascular disease is then detected in its initial stages, which is
essential to its effective treatment. The system developed in this study uses recorded
heart sounds and electrocardiogram signals to distinguish between normal
and abnormal heart conditions. This system improves on standard diagnostic tools
in that it does not require cumbersome and expensive imaging equipment or a
highly trained operator.
Heart sound- and electrocardiogram signals from 62 volunteers were recorded
with the prototype Precordialcardiogram device as part of a clinical study to aid in
the development of the autonomous auscultation software and to screen patients
for cardiovascular disease. These volunteers consisted of 28 patients of Tygerberg
Hospital with cardiovascular disease and, for control purposes, 34 persons with
normal heart conditions.
The autonomous auscultation system developed during this study, interprets
data obtained with the Precordialcardiogram device to autonomously acquire a
normal or abnormal diagnosis. The system employs wavelet soft thresholding to
denoise the recorded signals, followed by the segmentation of heart sound by
identifying peaks in the electrocardiogram. Novel frequency spectral information
was extracted as features from the heart sounds, by means of ensemble empirical
mode decomposition and auto regressive modelling. These features proved to be
particularly significant and played a major role in the screening capability of the
system. New time domain based features were identified, established on the specific
characteristics of the various cardiovascular diseases encountered during the
study. These features were extracted via the energy ratios between different parts
of ventricular systole and diastole of each recorded cardiac cycle.
The respective features were classified to characterise typical heart diseases as
well as healthy hearts with an ensemble artificial neural network. Herein the decisions
of all the members were combined to obtain a final diagnosis. The performance
of the autonomous auscultation system used in concert with the Precordialcardiogram
device prototype, as determined through the leave-one-out crossvalidation
method, had a sensitivity rating of 82% and a specificity rating of 88%.
These results demonstrate the potential benefit of the Precordialcardiogram device
and the developed autonomous auscultation software in a Telemedicine environment. / AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die navorsing van 'n outonome toetsing en sifting stelsel
vir kardiovaskulêre siektes in landelike dele van Afrika, vanwaar mediese inligting
per telefoon versend kan word. Die apparaat maak vroeë opsporing van kardiovaskulêre
siektes moontlik, wat essensieel is vir effektiewe behandeling daarvan
en ook die koste-effek van hierdie siektes verminder. In die huidige ontwikkelde
stelsel word normale sowel as abnormale hart-toestande getipeer met opnames
van hartklanke sowel as elektrokardiogram-seine. Voordele wat hierdie
stelsel bo standaard diagnostiese metodes het, sluit die hanteerbare formaat van
die hele apparaat sowel as die nie-noodsaaklikheid van duur beeldskeppende apparaat,
of hoogs opgeleide personeel.
Hartklank- en elektrokardiogramseine van 62 vrywilligers is met die prototipe
"Precordialcardiogram" apparaat opgeneem om by te dra tot die ontwikkeling van
die rekenaar sagteware vir die outonome auscultatsie stelsel en om die pasiëntsiftingsvermoë
daarvan te toets. Die vrywilligers het 28 pasiënte van Tygerberg
hospitaal met abnormale harttoestande ingesluit, sowel as ‘n kontrolegroep van 34
persone met normale harttoestande. Die outonome auskultasie-stelsel wat tot stand
gekom het deur hierdie ondersoek maak gebruik van “wavelet” sagte drempeling
om geraas uit die opgeneemde seine te verwyder. Daarna word die hartklanke gesegmenteer
deur die pieke van die elektrokardiogram te identifiseer.
Deur middel van "ensemble empirical mode decomposition" en outoregressiewe
modellering, is nuwe inligting aangaande die frekwensie spektra van
hartklanke, aanwysend van spesifieke harttoestande, verkry. Die beduidendheid
van hierdie eienskappe is bewys en het 'n belangrike rol in die siftingsvermoë van
die stelsel gespeel. Hierbenewens is nuwe tyd-gebaseerde eienskappe van die
onderskeie kardiovaskulêre siektes wat tydens die ondersoek bestudeer is, geïdentifiseer.
Hierdie eienskappe is geëien deur die energie-verhoudings tussen verskillende
dele van die ventrikulêre sistolie en diastolie van elke opgeneemde hartsiklus
te ontleed.
'n "Ensemble artificial neural network" is gebruik om die geïdentifiseerde eienskappe
van hartsiektes sowel as normale harttoestande, te klassifiseer. Hierin is
besluite van al die lede van die netwerk gekombineer, ten einde ‘n finale diagnose
te maak. Die klassifiseerder se geldigheid is kruis-bevestig deur middel van
die laat-een-uit kruisbevestigings-metode.
Deur middel van die kruis-bevestigingsmetode is die bedryfsvermoëns van die
outonome auskultasie-stelsel, toegerus met die "Precordialcardiogram" apparaat,
repektiewelik op 82% vir sensitiwiteit en 88% vir spesifisiteit vasgestel. Hierdie resultate demonstreer die benuttingspotensiaal van die apparaat in 'n Telemedisyne
omgewing.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/4239 |
| Date | 03 1900 |
| Creators | Botha, J. S. F. |
| Contributors | Scheffer, C., University of Stellenbosch. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 90 p. : ill. |
| Rights | University of Stellenbosch |
Page generated in 0.0027 seconds